Test_metacell

This is used to extract data from meta cell

Extract gene expression

features = read.table("/mnt/raid62/Lung_cancer_10x/MetaCell/immu_all/figs/immu_all_mc_f.cells_heat_marks_gene_names.txt")
features

##              x
## 1        FABP4
## 2        APOC1
## 3          FTL
## 4       MALAT1
## 5          GRN
## 6        CCL18
## 7          FN1
## 8         RETN
## 9        CXCR4
## 10        RGS1
## 11        CD69
## 12       TXNIP
## 13       CXCL3
## 14    HLA-DRB5
## 15      FCGR3A
## 16         TXN
## 17        RGCC
## 18        MT2A
## 19         VIM
## 20       CRIP1
## 21         FOS
## 22        SGK1
## 23        NPC2
## 24       ZFP36
## 25      GPR183
## 26        C1QA
## 27      TYROBP
## 28        CD3D
## 29         CD2
## 30        C1QB
## 31        C1QC
## 32      FCER1G
## 33      RNASE1
## 34        TRAC
## 35        CCL2
## 36        SPP1
## 37      LGALS1
## 38       RPL10
## 39       ITM2A
## 40        APOE
## 41        IFI6
## 42      CXCL10
## 43      RPL27A
## 44        MT1G
## 45       PLIN2
## 46        CCL3
## 47       RPL21
## 48       RPS27
## 49       RPS29
## 50     TSC22D3
## 51       ISG15
## 52      IFITM3
## 53    C15orf48
## 54     ZFP36L2
## 55      S100A8
## 56       SYNE2
## 57       TRBC1
## 58      S100A9
## 59        AREG
## 60        EREG
## 61        SAT1
## 62       CXCL8
## 63       CXCL2
## 64       PLAUR
## 65        G0S2
## 66        IL1B
## 67       TIMP1
## 68        LST1
## 69         LYZ
## 70        FCN1
## 71        CSTA
## 72        AIF1
## 73    MARCKSL1
## 74      RPS4Y1
## 75       BIRC3
## 76       CCL22
## 77         GSN
## 78       CCL17
## 79     WFDC21P
## 80       S100B
## 81    HLA-DQB2
## 82        CST3
## 83    HLA-DPB1
## 84    HLA-DPA1
## 85    HLA-DQA1
## 86        CD74
## 87    HLA-DRB1
## 88     HLA-DRA
## 89    HLA-DQB1
## 90      TUBA1B
## 91        CD1A
## 92      FCER1A
## 93    HLA-DQA2
## 94      INSIG1
## 95       NR4A3
## 96       RPS21
## 97        SRGN
## 98      VPREB3
## 99       CD79B
## 100      MS4A1
## 101    TSPAN13
## 102       SELL
## 103      PTGDS
## 104      ANXA1
## 105       IL32
## 106      TCL1A
## 107       TCF4
## 108       IRF8
## 109     HSPA1B
## 110      HSPB1
## 111      HSPA6
## 112      COTL1
## 113     ZNF331
## 114     JCHAIN
## 115      CD79A
## 116       XBP1
## 117      ITM2C
## 118        JUN
## 119       MZB1
## 120       SSR4
## 121     FKBP11
## 122      DERL3
## 123    HERPUD1
## 124    HSP90B1
## 125      KRT17
## 126     S100A2
## 127      KRT6A
## 128        TTR
## 129   MIR205HG
## 130     S100A4
## 131      WFDC2
## 132       CD52
## 133     SLC2A3
## 134       ELF3
## 135       PIGR
## 136      RPS12
## 137      CLDN4
## 138      SFTPB
## 139    SCGB3A2
## 140     SFTPA2
## 141      RPLP2
## 142      ISG20
## 143        B2M
## 144       HPGD
## 145      KRT19
## 146      GATA2
## 147      LTC4S
## 148      TPSB2
## 149       CPA3
## 150     TPSAB1
## 151      HPGDS
## 152        CLU
## 153      RPS3A
## 154       LMNA
## 155      APOA1
## 156     GIMAP7
## 157       MT1F
## 158     CCL3L1
## 159     CCL4L2
## 160      CXCR3
## 161       GZMK
## 162       CD8B
## 163       CD8A
## 164       CD27
## 165     ZNF683
## 166  LINC01871
## 167       GNLY
## 168      KLRB1
## 169      KLRD1
## 170       TRDC
## 171      KLRC1
## 172       XCL1
## 173       CST7
## 174      PLAC8
## 175       CMC1
## 176       NKG7
## 177       CTSW
## 178       XCL2
## 179       PRF1
## 180     FGFBP2
## 181       CCL4
## 182       HOPX
## 183       GZMB
## 184       MT1X
## 185       GYG1
## 186       GZMH
## 187      TRGC2
## 188       CCL5
## 189     SAMSN1
## 190     RPL36A
## 191     CNOT6L
## 192     CXCL13
## 193        CD7
## 194       GZMA
## 195      DUSP4
## 196        MX1
## 197       LY6E
## 198       PCNA
## 199    TNFRSF9
## 200       IFNG
## 201      PCLAF
## 202      HMGN2
## 203      STMN1
## 204      HMGB2
## 205       MT1E
## 206      DUSP1
## 207       RBPJ
## 208  LINC01943
## 209    ANKRD28
## 210      NR3C1
## 211       CREM
## 212      CXCR6
## 213     GPR171
## 214   LEPROTL1
## 215      TRAT1
## 216    ALOX5AP
## 217        MAF
## 218   HIST1H4C
## 219       TUBB
## 220   TNFRSF18
## 221      IL2RA
## 222       BATF
## 223      CTLA4
## 224        LTB
## 225      TIGIT
## 226     SPOCK2
## 227       PMCH
## 228       ICA1
## 229        NMB
## 230       ICOS
## 231     PMAIP1
## 232    TNFRSF4
## 233     MAGEH1
## 234   HSP90AA1
## 235     HSPA1A
## 236     DNAJB1
## 237      HSPE1
## 238      HSPH1
## 239     DNAJA1
## 240      HSPA8
## 241 AC016831.5
## 242     PTGER4
## 243     CD40LG
## 244       TOB1
## 245       IL7R
## 246       KLF2
## 247      RPL17
## 248     IFITM1
## 249       CCR7
## 250       JUNB
## 251      YPEL5
## 252      RPS26
## 253      EIF3E
## 254       PASK
## 255       XIST

load("/mnt/raid62/Lung_cancer_10x/MetaCell/immu_all/mc2d.immu_all_2dproj.Rda")
graph = object

load("/mnt/raid62/Lung_cancer_10x/MetaCell/immu_all/mc.immu_all_mc_f.Rda")
lfp = object

load("/mnt/raid62/Lung_cancer_10x/MetaCell/immu_all/mat.immu_all.Rda")
raw = object

Useful functions

Calculate the scale data, used by feature heatmaps

calculate_scale_data <- function(base_dir, smoo=FALSE, genes.use = NULL) {
    # read data
    # file_path = list.files(base_dir, pattern = "mc2d.*.Rda", full.names = T)
    # load(file_path)
    # graph = object
    # 
    # file_path = list.files(base_dir, pattern = "mc.*mc_f.Rda", full.names = T)
    # load(file_path)
    # lfp = object
    # 
    # file_path = list.files(base_dir, pattern = "mat.*.Rda", full.names = T)
    # load(file_path)
    # raw = object
    
    if (is.null(genes.use)) {
        genes.use = rownames(mat)
    }

    # start calculation
    mc_ord = 1:ncol(lfp@mc_fp)
    cell_ord = names(lfp@mc)[order(order(mc_ord)[lfp@mc])]
    
    mcp_heatmap_ideal_umi = quantile(colSums(as.matrix(raw@mat)), 0.25)
    
    raw_mat = as.matrix(raw@mat[genes.use, cell_ord])
    
    totu = colSums(as.matrix(raw@mat)[, cell_ord])
    raw_mat = t(t(raw_mat)/totu)*mcp_heatmap_ideal_umi
    lus_1 = log2(1+7*raw_mat)
    lus = apply(lus_1 - apply(lus_1, 1, median),2, function(x) pmax(x,0))
    
    if (smoo ) {
        smooth_n = max(2,ceiling(2 * length(cell_ord) / max(min(3000,length(cell_ord)+200),800)))
        lus_smoo = t(apply(lus, 1, function(x) rollmean(x,smooth_n, fill=0)))
        return(lus_smoo)
    } else {
        return (lus)
    }
}

Try feature heatmap

knitr::include_graphics("/mnt/raid62/Lung_cancer_10x/MetaCell/immu_all/figs/immu_all_mc_f.cells_heat_marks.png")

# mc_ord = 1:ncol(lfp@mc_fp)
# cell_ord = names(lfp@mc)[order(order(mc_ord)[lfp@mc])]
# 
# mcp_heatmap_ideal_umi = quantile(colSums(as.matrix(raw@mat)), 0.25)
# 
# raw_mat = as.matrix(raw@mat[rev(features$x), cell_ord])
# 
# totu = colSums(as.matrix(raw@mat)[, cell_ord])
# raw_mat = t(t(raw_mat)/totu)*mcp_heatmap_ideal_umi
# lus_1 = log2(1+7*raw_mat)
# lus = apply(lus_1 - apply(lus_1, 1, median),2, function(x) pmax(x,0))
# 
# smooth_n = max(2,ceiling(2 * length(cell_ord) / max(min(3000,length(cell_ord)+200),800)))
# lus_smoo = t(apply(lus, 1, function(x) rollmean(x,smooth_n, fill=0)))

lus = calculate_scale_data("/mnt/raid62/Lung_cancer_10x/MetaCell/immu_all/", genes.use = rev(features$x))
Heatmap(
    lus, 
    cluster_rows = F, 
    cluster_columns = F, 
    show_column_names = F,
    col = colorRamp2(c(0, 3, 6), c("white", "yellow", "red"))
)

lus_smoo <- calculate_scale_data("/mnt/raid62/Lung_cancer_10x/MetaCell/immu_all/", smoo = T, genes.use = rev(features$x))
Heatmap(
    lus_smoo, 
    cluster_rows = F, 
    cluster_columns = F, 
    show_column_names = F,
    col = colorRamp2(c(0, 3, 6), c("white", "yellow", "red"))
)

find out the batch infomation

va = HeatmapAnnotation(
    Batch = sapply(colnames(lus_smoo), function(x) {
        if(str_detect(x, "^2018")) {
            return("First")
        } else {
            return("Second")
        }
    })
)

Heatmap(
    lus_smoo, 
    cluster_rows = F, 
    cluster_columns = F, 
    show_column_names = F,
    col = colorRamp2(c(0, 3, 6), c("white", "yellow", "red")),
    bottom_annotation = va
)

Try Dots

knitr::include_graphics("/mnt/raid62/Lung_cancer_10x/MetaCell/immu_all/figs/immu_all_2dproj.2d_graph_proj.png")

temp = data.frame(
    x=graph@mc_x,
    y=graph@mc_y,
    l=1:length(graph@mc_x)
)



p <- ggplot(data = temp, aes(x=x, y=y, label=l)) + geom_point() +
    geom_label(aes(fill = l, boxstyle='circle'), colour = "white")

## Warning: Ignoring unknown aesthetics: boxstyle

# for(i in )
p

Do tSNE

lungcancer <- read.xlsx("/mnt/raid62/Lung_cancer_10x/MetaCell/20190627_lfp.xlsx", sheet = 3)
guo2018 <- read.xlsx("/mnt/raid62/Lung_cancer_10x/MetaCell/20190627_lfp.xlsx", sheet = 4)

format data

colnames(lungcancer) <- paste0("L", colnames(lungcancer))
colnames(guo2018) <- paste0("G", colnames(guo2018))

data <- merge(lungcancer, guo2018, by.x = "Lmc_id", "Gmc_id")
rownames(data) <- data$Lmc_id
data <- data[, !colnames(data) %in% c("Lmc_id")]

PCA

pca = prcomp(t(data))

pca_col = data.frame(
    mc_id=c(colnames(lungcancer)[2:ncol(lungcancer)], colnames(guo2018)[2:ncol(guo2018)]),
    source=c(rep("L", (ncol(lungcancer) - 1)), rep("G", (ncol(guo2018)) - 1))
)
rownames(pca_col) <- pca_col$mc_id

plist = list()
for(i in 1:(min(ncol(pca$x), 10) - 1)) {
    plist[[i]] <- autoplot(pca, data=pca_col, colour = "source", x=i, y=i + 1)
}
plot_grid(plotlist = plist, ncol = 4)

Select pca

temp = as.data.frame(apply(pca$x, 2, sd))
temp$x = rownames(temp)
colnames(temp) <- c("y", "x")

temp$x = factor(temp$x, levels = temp$x)

ggplot(data = temp[1:100,], aes(x=x, y=y)) + 
    geom_point() + 
    theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
    labs(x = "", y = "")

tsne = Rtsne(X=pca$x[, 1:10], dims = 2)

tsne_coord = as.data.frame(tsne$Y)
tsne_coord$x = rownames(pca$x)
tsne_coord$source = sapply(tsne_coord$x, function(x) {
   str_replace(as.character(x), "\\d+", "")
})
colnames(tsne_coord) <- c("tSNE_1", "tSNE_2", "mc_id", "source")


ggplot(
    tsne_coord, 
    aes(x=tSNE_1, y=tSNE_2, color = source)
) + 
    geom_point()

make feature plot

known_markers <- read.xlsx("/mnt/raid62/Lung_cancer_10x/MetaCell/20190628_gene_markers.xlsx", sheet = 1)

known_markers <- known_markers[known_markers$gene %in% features$x, ]

---

Why just using Seurat

obj <- CreateSeuratObject(
    data, 
    meta.data = data.frame(
        source = sapply(colnames(data), function(x) {
            str_replace(as.character(x), "\\d+", "")
        }),
        cell_id = colnames(data)
    )
)

### 1. vlnplot
mito.genes <- grep(pattern = "^MT-", x = rownames(x = obj@raw.data), value = TRUE)
percent.mito <- Matrix::colSums(obj@raw.data[mito.genes, ])/Matrix::colSums(obj@raw.data)

# AddMetaData adds columns to object@meta.data, and is a great place to
# stash QC stats
obj <- AddMetaData(object = obj, metadata = percent.mito, col.name = "percent.mito")

VlnPlot(object = obj, features.plot = c("nGene", "nUMI", "percent.mito"), nCol = 3)

## Warning in SingleVlnPlot(feature = x, data = data.use[, x, drop = FALSE], :
## All cells have the same value of feature.

par(mfrow = c(1, 2))
GenePlot(object = obj, gene1 = "nUMI", gene2 = "percent.mito")

## Warning in cor(x = data.plot$x, y = data.plot$y): the standard deviation is
## zero

GenePlot(object = obj, gene1 = "nUMI", gene2 = "nGene")

The expression value already scaled, using raw.data as scale.data

# obj <- NormalizeData(object = obj, normalization.method = "LogNormalize", scale.factor = 10000)
obj <- FindVariableGenes(object = obj, mean.function = ExpMean, dispersion.function = LogVMR, x.low.cutoff = 0.0125, x.high.cutoff = 3, y.cutoff = 0.5)

# obj <- ScaleData(object = obj, vars.to.regress = c("nUMI", "percent.mito"))
obj@scale.data = obj@raw.data

obj <- RunPCA(object = obj, pc.genes = obj@var.genes, do.print = FALSE, pcs.compute = 100)

## Warning in irlba(A = t(x = data.use), nv = pcs.compute, ...): You're
## computing too large a percentage of total singular values, use a standard
## svd instead.

select PCs

PCElbowPlot(obj, num.pc=100)  +
    scale_x_continuous(breaks=1:100) + 
    theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))

Run tSNE

n.pcs = 10

obj <- RunTSNE(object = obj, dims.use = 1:n.pcs, do.fast = TRUE, reduction.use = "pca", reduction.name = "tsne")
obj <- FindClusters(object = obj, reduction.type = "pca", dims.use = 1:n.pcs, resolution = 0.6, print.output = 0, save.SNN = FALSE, force.recalc = TRUE)

Check the clusters

p1 <- DimPlot(
    obj, 
    reduction.use = "tsne", 
    group.by = "res.0.6", 
    no.legend = TRUE, 
    do.label = TRUE, 
    label.size = 8, 
    do.return = TRUE
)

p2 <- DimPlot(
    obj, 
    reduction.use = "tsne", 
    group.by = "source", 
    no.legend = TRUE, 
    do.label = TRUE, 
    label.size = 8, 
    do.return = TRUE
)

plot_grid(p1, p2, nrow = 1)

FeaturePlot(
    obj, features.plot = intersect(known_markers$gene, rownames(obj@raw.data)),
    cols.use = c("grey", "blue")
)

known_markers[known_markers$gene %in% rownames(obj@raw.data), ]

##          group    gene   color priority T_fold
## 6      B cells   MS4A1 #9CB8DD        2      1
## 10        CD4+     CD2 #EE6719        5      1
## 11        CD4+    CD3D #EE6719        2      1
## 16        CD8+    CD8A #278F36        1      1
## 17        CD8+    CD8B #278F36        2      1
## 18        CD8+    GZMK #278F36        3      1
## 30   Dendritic    CCR7 #EF7E82        3      1
## 58   Exhaust T   TIGIT #F1A3C5        2      1
## 61   Exhaust T   CTLA4 #F1A3C5        2      1
## 79   Monocytes  FCGR3A     red        2      1
## 83          NK    GNLY #D1D57A        1      1
## 84          NK    NKG7 #D1D57A        1      1
## 90  Alveolar_I    HOPX #8BCFDA        4      1
## 110       Treg   CTLA4    blue        2      1
## 112       Treg   IL2RA    blue        1      1
## 113       Treg     LTB    blue        2      1
## 114       Treg TNFRSF4    blue        1      1

Identify cell types

cell_types = c(
    "0"="Monocytes",
    "1"="CD4",
    "2"="CD8",
    "3"="CD8",
    "4"="CD4",
    "5"="B cells",
    "6"="Unknown"
)

obj@meta.data$cell = sapply(obj@meta.data$res.0.6, function(x) {
    cell_types[[x]]
})

Load meta of Guo2018

guo2018_meta = read.xlsx("/mnt/raid62/Lung_cancer_10x/MetaCell/20190627_lfp.xlsx", sheet = 5, colNames = F)
colnames(guo2018_meta) = sapply(guo2018_meta[3,], function(x) {
    paste0("G", x)
})

guo2018_meta = as.data.frame(t(guo2018_meta))
colnames(guo2018_meta) <- guo2018_meta[1, ]
guo2018_meta = guo2018_meta[2:nrow(guo2018_meta), ]

guo2018_meta$cell = apply(guo2018_meta, 1, function(row) {
    if(as.numeric(row[2]) == 0) {
        return("CD8")
    } else if(as.numeric(row[1]) == 0) {
        return("CD4")
    } else {
        return("Mix")
    }
})

obj@meta.data$guo = guo2018_meta[obj@meta.data$cell_id, "cell"]

Check if CD4 and CD8 consistent with Guo2018

p1 <- DimPlot(
    obj, 
    reduction.use = "tsne", 
    group.by = "cell", 
    no.legend = TRUE, 
    do.label = TRUE, 
    label.size = 8, 
    do.return = TRUE
)


p2 <- DimPlot(
    obj, 
    reduction.use = "tsne", 
    group.by = "guo", 
    no.legend = FALSE, 
    do.label = FALSE, 
    label.size = 8, 
    do.return = TRUE
)

plot_grid(p1, p2, nrow = 1, rel_widths = c(1, 1.2))

# temp = as.data.frame(obj@dr$tsne@cell.embeddings)
# temp$source = obj@meta.data[rownames(temp), "guo"]
# temp$source[is.na(temp$source)] = ""
# 
# temp$cell = obj@meta.data[rownames(temp), "cell"]
# p2 <- ggplot(data = temp, aes(x=tSNE_1, y=tSNE_2, color=cell, shape = source)) + 
#     geom_point() + 
#     theme_bw() 

Using all markers genes to plots

all_markers = read.xlsx("/mnt/raid62/Lung_cancer_10x/MetaCell/20190628_gene_markers.xlsx", sheet = 1)
all_markers = all_markers[all_markers$gene %in% rownames(obj@raw.data), ]

for(i in unique(all_markers$group)) {
    FeaturePlot(obj, features.plot=all_markers$gene[all_markers$group == i], cols.use = c("grey", "blue"))
}

Using FindMarkers to find highly expressed genes in cluster6

find_markers <- function(object, group.by = "Stage", ...) {
    
    temp <- sort(unique(object@meta.data[, group.by]))
    res = NULL
    for(i in temp)  {
        groups = rownames(object@meta.data[object@meta.data[, group.by] == i, ])
        
        if(length(groups) >= 3) {
            temp <- FindMarkers(
                object = object, 
                ident.1 = groups,
                ...
            )
            
            temp$ident = i
            temp$gene = rownames(temp)
            res = rbind(res, temp)
        }
    }
    
    return(res)
}

markers = find_markers(obj, group.by = "res.0.6", test.use = "t")

temp = markers[markers$ident == 6, ]
temp[order(temp$avg_logFC, decreasing = T), ]

##                      p_val  avg_logFC pct.1 pct.2     p_val_adj ident
## HERPUD15      1.717564e-23  3.3583146  1.00 0.476  1.102676e-20     6
## HSPB15        2.795456e-07  2.9411076  0.92 0.481  1.794683e-04     6
## ITM2C6        2.084639e-12  2.3199759  0.96 0.476  1.338338e-09     6
## PIM24         1.392682e-19  1.8752731  1.00 0.475  8.941019e-17     6
## PDIA63        1.393011e-14  1.7908917  1.00 0.478  8.943131e-12     6
## RABAC15       4.541942e-12  1.7501814  0.96 0.478  2.915927e-09     6
## JUN3          3.508951e-09  1.7399033  0.88 0.484  2.252747e-06     6
## GSTP15        1.447234e-14  1.2845654  1.00 0.478  9.291244e-12     6
## IFI64         1.975329e-06  1.2738422  0.96 0.479  1.268161e-03     6
## CD381         6.621064e-13  1.2721450  0.96 0.466  4.250723e-10     6
## COPE2         3.663347e-17  1.1578039  1.00 0.473  2.351869e-14     6
## CCNL1         8.615276e-01  1.1281335  0.32 0.496  1.000000e+00     6
## ISG204        4.973473e-09  1.1084497  0.96 0.473  3.192969e-06     6
## SLAMF73       6.562927e-13  1.0565555  0.96 0.470  4.213399e-10     6
## SLC3A21       1.846660e-07  1.0512458  1.00 0.475  1.185556e-04     6
## CD276         1.026707e-06  0.9562570  0.92 0.482  6.591458e-04     6
## DAD11         1.179484e-12  0.8894520  1.00 0.473  7.572289e-10     6
## PRDX52        6.941399e-08  0.8157394  0.92 0.476  4.456378e-05     6
## BST24         9.828716e-11  0.7975092  0.96 0.481  6.310036e-08     6
## MALAT13       1.814571e-05  0.7789842  0.92 0.484  1.164954e-02     6
## NME11         9.947729e-11  0.7764932  0.96 0.473  6.386442e-08     6
## GLRX6         4.376722e-08  0.7744490  0.96 0.478  2.809855e-05     6
## TPST23        1.688647e-09  0.7590536  0.96 0.476  1.084112e-06     6
## CD79B6        2.216573e-11  0.7420985  0.96 0.479  1.423040e-08     6
## OST41         1.597090e-07  0.7186273  0.84 0.481  1.025332e-04     6
## VSIR4         2.005233e-07  0.7034811  0.96 0.478  1.287360e-04     6
## SUB14         1.017032e-09  0.6978142  0.96 0.476  6.529343e-07     6
## COX5A3        2.574207e-07  0.6655310  0.96 0.478  1.652641e-04     6
## CYTOR6        9.441603e-06  0.6540164  0.92 0.482  6.061509e-03     6
## FDPS          3.162322e-05  0.6530278  1.00 0.470  2.030211e-02     6
## COX7A21       4.986745e-04  0.6418140  0.84 0.479  3.201490e-01     6
## TRMT1122      1.323196e-05  0.6189527  0.88 0.481  8.494919e-03     6
## RPLP04        4.188108e-10  0.6186207  0.92 0.481  2.688765e-07     6
## CALR3         8.773481e-06  0.6129590  0.92 0.479  5.632575e-03     6
## RPL36AL1      7.114541e-04  0.6064923  0.92 0.478  4.567536e-01     6
## UBB5          1.503271e-02  0.5946195  0.76 0.487  1.000000e+00     6
## CD636         7.971204e-07  0.5512023  0.92 0.484  5.117513e-04     6
## MRPL511       7.116597e-07  0.5495319  0.96 0.476  4.568855e-04     6
## CCND24        3.845369e-05  0.5168451  0.80 0.485  2.468727e-02     6
## CDK2AP21      1.731082e-07  0.4749713  0.96 0.479  1.111355e-04     6
## DSTN4         7.790807e-07  0.4620131  0.92 0.476  5.001698e-04     6
## PDIA32        1.861139e-05  0.4319515  0.88 0.481  1.194851e-02     6
## MIF           3.880773e-02  0.4313511  0.68 0.491  1.000000e+00     6
## PSMD82        4.541894e-05  0.4176265  0.92 0.475  2.915896e-02     6
## IFI351        2.315273e-05  0.4072310  0.92 0.478  1.486405e-02     6
## TRADD         1.932687e-07  0.3976770  0.92 0.473  1.240785e-04     6
## NCF42         1.908908e-08  0.3973867  0.92 0.482  1.225519e-05     6
## RPL85         4.728107e-05  0.3961866  0.88 0.484  3.035445e-02     6
## DDIT44        6.811168e-02  0.3825302  0.56 0.494  1.000000e+00     6
## PRDX15        1.581565e-09  0.3819379  1.00 0.475  1.015365e-06     6
## RPL7A3        6.141163e-04  0.3814993  0.92 0.478  3.942626e-01     6
## PSMB62        4.847125e-05  0.3803966  0.92 0.475  3.111854e-02     6
## NDUFA11       1.529392e-02  0.3728713  0.72 0.484  1.000000e+00     6
## NDUFA41       3.663610e-02  0.3621901  0.72 0.485  1.000000e+00     6
## ISG153        2.020181e-02  0.3548192  0.76 0.488  1.000000e+00     6
## CENPX1        6.745805e-01  0.3376507  0.44 0.490  1.000000e+00     6
## PSME23        4.996766e-03  0.3329286  0.88 0.478  1.000000e+00     6
## VCP2          1.735652e-03  0.3074135  0.84 0.479  1.000000e+00     6
## PLP22         3.836168e-07  0.2863633  0.88 0.481  2.462820e-04     6
## RPLP13        1.785349e-02  0.2821762  0.80 0.484  1.000000e+00     6
## SURF44        4.640510e-05  0.2801058  0.96 0.473  2.979208e-02     6
## KIF22         5.026249e-02  0.2796804  0.88 0.473  1.000000e+00     6
## UQCRC13       4.135773e-03  0.2712530  0.84 0.482  1.000000e+00     6
## COX5B3        5.670641e-03  0.2609616  0.80 0.484  1.000000e+00     6
## KIAA15512     6.920047e-04 -0.2503880  0.04 0.512  4.442670e-01     6
## RPS144        1.377760e-01 -0.2534653  0.44 0.501  1.000000e+00     6
## ATP1B35       2.001386e-03 -0.2539341  0.08 0.513  1.000000e+00     6
## P2RY102       4.029710e-09 -0.2549336  0.04 0.510  2.587074e-06     6
## IFI441        1.106327e-05 -0.2557693  0.16 0.501  7.102620e-03     6
## LEF13         7.484161e-28 -0.2560654  0.24 0.497  4.804831e-25     6
## ANXA62        1.062210e-02 -0.2562932  0.24 0.504  1.000000e+00     6
## NABP12        1.291329e-06 -0.2572273  0.12 0.500  8.290331e-04     6
## ZFP364        1.874985e-01 -0.2573897  0.48 0.499  1.000000e+00     6
## TMEM1233      1.055641e-03 -0.2605991  0.04 0.512  6.777212e-01     6
## A2M4          8.871378e-19 -0.2633548  0.40 0.395  5.695424e-16     6
## LINC008923    1.466130e-38 -0.2633723  0.08 0.504  9.412555e-36     6
## EIF4A34       1.478688e-02 -0.2634301  0.36 0.497  1.000000e+00     6
## PSMA44        2.220534e-03 -0.2701599  0.20 0.504  1.000000e+00     6
## SRP141        6.048681e-04 -0.2705094  0.20 0.503  3.883253e-01     6
## SASH3         4.175835e-12 -0.2728939  0.04 0.503  2.680886e-09     6
## PNISR2        1.231013e-02 -0.2736827  0.24 0.506  1.000000e+00     6
## RPS184        1.128777e-01 -0.2753025  0.40 0.501  1.000000e+00     6
## NOP533        8.539647e-02 -0.2753518  0.40 0.499  1.000000e+00     6
## LINC008613    1.832525e-23 -0.2802728  0.04 0.509  1.176481e-20     6
## PTGER21       5.414027e-50 -0.2844689  0.04 0.510  3.475805e-47     6
## EIF3L2        1.366648e-03 -0.2887073  0.16 0.509  8.773882e-01     6
## GBP53         2.042087e-09 -0.2900546  0.12 0.507  1.311020e-06     6
## ANP32B        2.677871e-04 -0.2904114  0.12 0.506  1.719193e-01     6
## ITGA15        1.886677e-40 -0.2911237  0.12 0.493  1.211247e-37     6
## RPS104        7.116776e-02 -0.2913298  0.40 0.501  1.000000e+00     6
## RAN2          1.022273e-03 -0.2918589  0.24 0.503  6.562991e-01     6
## NFKBIA6       9.038439e-01 -0.2977365  0.68 0.490  1.000000e+00     6
## EIF3E4        9.429381e-03 -0.2991537  0.28 0.500  1.000000e+00     6
## TSEN542       6.671633e-18 -0.3038779  0.00 0.512  4.283188e-15     6
## APMAP5        9.372149e-07 -0.3073751  0.20 0.497  6.016919e-04     6
## PA2G42        3.980178e-06 -0.3083921  0.12 0.509  2.555274e-03     6
## RGS15         4.809235e-01 -0.3093756  0.52 0.497  1.000000e+00     6
## OASL4         1.548606e-16 -0.3193156  0.20 0.509  9.942054e-14     6
## STAT12        3.712646e-04 -0.3193959  0.20 0.504  2.383519e-01     6
## TCF72         2.883994e-09 -0.3216353  0.24 0.503  1.851524e-06     6
## LPXN1         1.900867e-09 -0.3301573  0.08 0.503  1.220357e-06     6
## COX6C1        1.700404e-03 -0.3331004  0.16 0.506  1.000000e+00     6
## TPI15         1.338501e-02 -0.3372821  0.28 0.504  1.000000e+00     6
## CXCR34        1.172511e-09 -0.3404141  0.12 0.510  7.527521e-07     6
## KPNA22        1.371542e-08 -0.3405695  0.12 0.504  8.805299e-06     6
## RPL134        2.478999e-02 -0.3417294  0.20 0.509  1.000000e+00     6
## SLBP2         9.821628e-12 -0.3437036  0.04 0.504  6.305485e-09     6
## RPS65         8.610894e-02 -0.3446196  0.40 0.499  1.000000e+00     6
## CRYBG1       1.122762e-121 -0.3470171  0.00 0.507 7.208132e-119     6
## PDCD14        2.064680e-38 -0.3472020  0.08 0.506  1.325525e-35     6
## RPS163        2.710768e-02 -0.3504102  0.16 0.509  1.000000e+00     6
## KLRF15        3.372968e-06 -0.3512241  0.36 0.392  2.165445e-03     6
## TMC81         5.422567e-21 -0.3512889  0.00 0.516  3.481288e-18     6
## PRKCQ-AS13    2.499217e-83 -0.3547966  0.00 0.512  1.604497e-80     6
## RHBDD22       1.829661e-07 -0.3586052  0.16 0.503  1.174642e-04     6
## PPP1CA5       9.457701e-06 -0.3603684  0.16 0.501  6.071844e-03     6
## PCNA3         2.459147e-10 -0.3618140  0.12 0.501  1.578772e-07     6
## CLDND13       1.230503e-06 -0.3623677  0.16 0.501  7.899832e-04     6
## FCMR4         4.762304e-39 -0.3630434  0.04 0.515  3.057399e-36     6
## ANP32E3       1.008498e-11 -0.3703155  0.04 0.507  6.474558e-09     6
## SIT13         4.945343e-09 -0.3704944  0.16 0.509  3.174910e-06     6
## LAIR25        1.930653e-26 -0.3707600  0.24 0.485  1.239479e-23     6
## PCLAF4        1.727840e-12 -0.3818235  0.32 0.466  1.109273e-09     6
## MYL64         3.798640e-05 -0.3827094  0.12 0.507  2.438727e-02     6
## RPL194        3.131116e-04 -0.3829162  0.20 0.500  2.010176e-01     6
## ARHGAP152     8.563343e-10 -0.3848187  0.04 0.512  5.497666e-07     6
## RPS26         9.789270e-03 -0.3894206  0.16 0.510  1.000000e+00     6
## TBC1D43       1.062765e-45 -0.3896463  0.00 0.507  6.822952e-43     6
## CRTAM6        3.272230e-18 -0.3901106  0.08 0.507  2.100771e-15     6
## KLRC25        1.844599e-18 -0.3922449  0.12 0.429  1.184232e-15     6
## DENND2D3      3.482864e-18 -0.3941887  0.04 0.509  2.235999e-15     6
## GSTK13        2.118864e-05 -0.3952993  0.08 0.506  1.360310e-02     6
## OCIAD23       8.811942e-05 -0.3967977  0.04 0.513  5.657267e-02     6
## AOAH5         2.838609e-65 -0.3983676  0.00 0.513  1.822387e-62     6
## SERINC52      3.162903e-25 -0.3999930  0.04 0.504  2.030584e-22     6
## CNN22         9.048687e-06 -0.4033278  0.04 0.516  5.809257e-03     6
## XIST1         2.180121e-08 -0.4045509  0.16 0.507  1.399638e-05     6
## GALM3         8.647906e-15 -0.4144016  0.00 0.512  5.551956e-12     6
## CLIC34        2.103200e-13 -0.4158048  0.32 0.501  1.350255e-10     6
## TNFRSF95      2.640656e-32 -0.4161950  0.04 0.507  1.695301e-29     6
## FLNA3         6.251156e-06 -0.4170795  0.04 0.510  4.013242e-03     6
## SIRPG4        4.820577e-40 -0.4183642  0.08 0.513  3.094810e-37     6
## DUT1          3.276275e-07 -0.4188665  0.08 0.506  2.103369e-04     6
## FGR5          1.427635e-58 -0.4228849  0.00 0.500  9.165420e-56     6
## SERPINB93     1.157486e-04 -0.4253907  0.24 0.503  7.431060e-02     6
## CD40LG4       6.488094e-47 -0.4277646  0.08 0.500  4.165356e-44     6
## RPL23A4       1.224677e-02 -0.4277846  0.20 0.504  1.000000e+00     6
## ZNF6835       1.162759e-23 -0.4292531  0.36 0.494  7.464911e-21     6
## CD582         3.314053e-21 -0.4295705  0.00 0.507  2.127622e-18     6
## PTTG15        6.841079e-12 -0.4326074  0.04 0.509  4.391973e-09     6
## CD441         3.209221e-03 -0.4342098  0.32 0.501  1.000000e+00     6
## UGP22         4.598577e-10 -0.4357099  0.00 0.510  2.952286e-07     6
## HPRT11        5.135360e-18 -0.4370408  0.00 0.509  3.296901e-15     6
## TANK3         1.540824e-09 -0.4394119  0.04 0.512  9.892088e-07     6
## CD56          2.432188e-50 -0.4425534  0.00 0.515  1.561464e-47     6
## APOBEC3C4     4.293116e-08 -0.4455356  0.16 0.510  2.756180e-05     6
## RPL143        5.052453e-05 -0.4465839  0.16 0.506  3.243675e-02     6
## SOD13         9.661178e-04 -0.4485050  0.20 0.510  6.202476e-01     6
## CBLB3         1.541163e-40 -0.4503770  0.00 0.507  9.894264e-38     6
## IFITM15       2.072839e-02 -0.4528031  0.24 0.507  1.000000e+00     6
## CTSA3         4.531359e-08 -0.4533791  0.12 0.512  2.909132e-05     6
## SOCS14        2.372499e-12 -0.4561648  0.04 0.510  1.523144e-09     6
## NPM13         1.447810e-05 -0.4567479  0.08 0.509  9.294940e-03     6
## SLC25A34      1.651722e-05 -0.4575334  0.16 0.506  1.060406e-02     6
## HNRNPM        2.415549e-12 -0.4585762  0.00 0.507  1.550782e-09     6
## SAMD35        1.821118e-62 -0.4597325  0.00 0.515  1.169158e-59     6
## PBXIP14       1.851764e-09 -0.4673128  0.12 0.513  1.188832e-06     6
## TPP14         4.709643e-11 -0.4679021  0.12 0.509  3.023591e-08     6
## STAT43        1.972746e-31 -0.4744518  0.00 0.513  1.266503e-28     6
## RPL274        1.013936e-03 -0.4763899  0.12 0.512  6.509467e-01     6
## RPL13A3       8.634023e-04 -0.4777350  0.08 0.506  5.543043e-01     6
## CASP13        4.032251e-11 -0.4803568  0.08 0.509  2.588705e-08     6
## IFNGR13       3.641054e-08 -0.4821873  0.04 0.512  2.337557e-05     6
## IL2RA6        5.034290e-30 -0.4823590  0.04 0.507  3.232014e-27     6
## YWHAQ3        1.364276e-07 -0.4893327  0.12 0.512  8.758652e-05     6
## OXNAD14       2.930106e-35 -0.4948393  0.00 0.513  1.881128e-32     6
## CTLA43        3.513802e-42 -0.4973195  0.04 0.512  2.255861e-39     6
## ITK4          9.066694e-65 -0.5000958  0.00 0.513  5.820818e-62     6
## PSMA73        6.831807e-06 -0.5007064  0.04 0.513  4.386020e-03     6
## TMEM50A3      1.567517e-07 -0.5017793  0.12 0.509  1.006346e-04     6
## RPL10A4       9.184927e-05 -0.5076833  0.08 0.515  5.896723e-02     6
## GSTO16        7.364361e-06 -0.5080880  0.16 0.509  4.727920e-03     6
## NINJ15        3.931194e-10 -0.5127315  0.20 0.507  2.523827e-07     6
## TNFAIP81      1.050617e-13 -0.5156934  0.00 0.515  6.744963e-11     6
## H2AFY5        2.134374e-06 -0.5239542  0.04 0.513  1.370268e-03     6
## TNFRSF255     1.542932e-54 -0.5304503  0.00 0.513  9.905626e-52     6
## JPT14         1.373881e-10 -0.5315001  0.04 0.510  8.820316e-08     6
## TUBB5         1.876970e-04 -0.5338852  0.12 0.509  1.205014e-01     6
## FKBP51        1.569278e-27 -0.5342096  0.00 0.512  1.007477e-24     6
## RPS235        5.974780e-04 -0.5377403  0.12 0.512  3.835808e-01     6
## TRIM22        3.130991e-25 -0.5409626  0.00 0.507  2.010096e-22     6
## UBE2N4        4.966509e-14 -0.5418090  0.00 0.515  3.188498e-11     6
## RPL115        2.167811e-06 -0.5429541  0.12 0.510  1.391735e-03     6
## GABARAPL13    2.143747e-12 -0.5431222  0.08 0.509  1.376285e-09     6
## LDHB3         5.535072e-06 -0.5437748  0.12 0.509  3.553516e-03     6
## BIN23         3.695819e-17 -0.5543739  0.00 0.515  2.372716e-14     6
## RPS135        3.793451e-03 -0.5578215  0.08 0.512  1.000000e+00     6
## RPL364        8.068692e-04 -0.5584701  0.20 0.507  5.180100e-01     6
## APOBEC3G6     6.270772e-06 -0.5584776  0.24 0.509  4.025836e-03     6
## ARGLU12       2.771086e-09 -0.5612044  0.00 0.510  1.779037e-06     6
## PRNP1         1.818790e-15 -0.5616376  0.00 0.513  1.167663e-12     6
## RPL233        1.169630e-04 -0.5616473  0.04 0.510  7.509028e-02     6
## HNRNPH12      7.181740e-07 -0.5622710  0.24 0.503  4.610677e-04     6
## PTPN224       2.291787e-31 -0.5633392  0.00 0.516  1.471327e-28     6
## RIPOR23       2.811341e-22 -0.5688735  0.04 0.510  1.804881e-19     6
## LYAR5         2.041522e-45 -0.5716712  0.00 0.510  1.310657e-42     6
## RPL264        2.231449e-05 -0.5722170  0.00 0.513  1.432590e-02     6
## KLRG15        4.453087e-36 -0.5733525  0.08 0.499  2.858882e-33     6
## RNF19A4       1.383472e-21 -0.5772546  0.00 0.509  8.881892e-19     6
## TNFRSF185     1.969427e-01 -0.5782474  0.64 0.493  1.000000e+00     6
## SNRPB2        4.970411e-10 -0.5796091  0.04 0.515  3.191004e-07     6
## ANXA55        1.144466e-04 -0.5798631  0.36 0.500  7.347470e-02     6
## HNRNPF1       2.857576e-12 -0.5842768  0.00 0.515  1.834564e-09     6
## STAT34        5.161187e-11 -0.5870717  0.00 0.515  3.313482e-08     6
## ABI32         1.706775e-46 -0.5885994  0.00 0.509  1.095750e-43     6
## RPS204        2.617525e-09 -0.5887359  0.04 0.513  1.680451e-06     6
## RHOA5         1.106849e-05 -0.5914631  0.12 0.506  7.105971e-03     6
## PRR13         5.152278e-11 -0.5920202  0.00 0.509  3.307762e-08     6
## SYTL34        1.780753e-35 -0.5932262  0.00 0.515  1.143243e-32     6
## IVNS1ABP2     4.735052e-20 -0.5940237  0.04 0.507  3.039903e-17     6
## AP2S16        1.355507e-05 -0.5947245  0.24 0.500  8.702355e-03     6
## TSC22D33      7.678127e-02 -0.6007794  0.32 0.503  1.000000e+00     6
## PAXX3         1.883993e-13 -0.6089127  0.00 0.512  1.209524e-10     6
## MCUB1         1.892339e-23 -0.6089995  0.00 0.516  1.214882e-20     6
## OSTF12        1.612837e-14 -0.6112355  0.00 0.507  1.035442e-11     6
## ENO15         1.166253e-06 -0.6114839  0.04 0.515  7.487342e-04     6
## XCL25         1.288353e-25 -0.6115003  0.44 0.487  8.271226e-23     6
## PDCD43        1.245571e-12 -0.6115602  0.00 0.516  7.996563e-10     6
## RHOC4         1.387728e-15 -0.6239551  0.00 0.512  8.909212e-13     6
## SH2D2A4       1.146445e-60 -0.6278563  0.00 0.513  7.360178e-58     6
## KLRC15        1.185399e-22 -0.6300195  0.32 0.485  7.610262e-20     6
## ZYX2          9.767665e-42 -0.6304357  0.00 0.512  6.270841e-39     6
## EFHD25        1.205773e-08 -0.6327349  0.04 0.515  7.741065e-06     6
## TRAF3IP34     9.437357e-17 -0.6341259  0.00 0.516  6.058783e-14     6
## UBA522        2.478957e-11 -0.6412699  0.00 0.512  1.591490e-08     6
## RPS215        1.499530e-04 -0.6424049  0.04 0.509  9.626981e-02     6
## CD824         6.620399e-29 -0.6437322  0.00 0.513  4.250296e-26     6
## TMEM1735      2.989765e-51 -0.6461029  0.00 0.513  1.919429e-48     6
## FTL6          9.508635e-07 -0.6512506  0.96 0.476  6.104544e-04     6
## TOB13         2.091585e-25 -0.6549974  0.00 0.513  1.342798e-22     6
## TXNIP4        2.493655e-04 -0.6563208  0.20 0.510  1.600926e-01     6
## PGK13         6.193520e-08 -0.6572629  0.08 0.512  3.976240e-05     6
## CD46         4.399923e-104 -0.6578825  0.00 0.510 2.824751e-101     6
## PYHIN14       2.249939e-60 -0.6617890  0.00 0.515  1.444461e-57     6
## ADGRE55       1.045293e-15 -0.6666384  0.00 0.518  6.710784e-13     6
## HAVCR24       4.608055e-52 -0.6668083  0.00 0.515  2.958371e-49     6
## RPS254        1.242108e-05 -0.6707025  0.16 0.510  7.974336e-03     6
## RPS15A3       3.454250e-05 -0.6708874  0.00 0.513  2.217629e-02     6
## RPL124        7.281847e-07 -0.6722975  0.00 0.510  4.674946e-04     6
## GAPDH5        5.200970e-05 -0.6728986  0.16 0.510  3.339023e-02     6
## RAP1B2        1.241978e-10 -0.6738006  0.04 0.504  7.973497e-08     6
## ABRACL3       1.193983e-10 -0.6754766  0.04 0.512  7.665370e-08     6
## SARAF3        1.161965e-04 -0.6770448  0.08 0.513  7.459815e-02     6
## RPL324        2.518693e-04 -0.6803482  0.00 0.515  1.617001e-01     6
## LCP22         3.968216e-62 -0.6813387  0.00 0.509  2.547595e-59     6
## CXCR65        1.614812e-39 -0.6853472  0.12 0.513  1.036709e-36     6
## LDHA3         3.486279e-06 -0.6898669  0.08 0.512  2.238191e-03     6
## LAG36         7.432031e-10 -0.6919606  0.32 0.503  4.771364e-07     6
## HLA-DRB65     4.492353e-02 -0.6922375  0.64 0.488  1.000000e+00     6
## MYO1F3        8.032456e-24 -0.6942737  0.00 0.512  5.156837e-21     6
## IL2RB4        1.723318e-57 -0.6955907  0.00 0.516  1.106370e-54     6
## LRRC75A-AS14  3.477838e-10 -0.7001915  0.04 0.512  2.232772e-07     6
## HNRNPA2B11    2.076707e-13 -0.7140406  0.00 0.507  1.333246e-10     6
## AES4          7.393725e-10 -0.7182585  0.04 0.513  4.746771e-07     6
## ARL6IP12      7.323092e-11 -0.7190808  0.04 0.516  4.701425e-08     6
## ITGB13        8.273153e-14 -0.7191144  0.00 0.510  5.311364e-11     6
## GPR1716       1.824581e-59 -0.7198532  0.00 0.516  1.171381e-56     6
## CDC42SE1      1.847882e-21 -0.7232083  0.00 0.512  1.186340e-18     6
## PMAIP14       1.040849e-06 -0.7248891  0.12 0.510  6.682247e-04     6
## PIP4K2A4      2.631859e-26 -0.7251095  0.00 0.513  1.689654e-23     6
## HMGN12        1.821299e-13 -0.7364079  0.00 0.513  1.169274e-10     6
## TNFRSF46      2.035227e-02 -0.7364594  0.60 0.491  1.000000e+00     6
## CCND32        2.675650e-32 -0.7459283  0.00 0.512  1.717767e-29     6
## BATF5         2.870344e-16 -0.7463318  0.04 0.513  1.842761e-13     6
## GBP23         1.007825e-29 -0.7532651  0.00 0.504  6.470235e-27     6
## RPS33         5.316337e-06 -0.7584886  0.00 0.515  3.413089e-03     6
## GAS53         4.943866e-12 -0.7599044  0.04 0.506  3.173962e-09     6
## SAMHD15       1.103322e-28 -0.7613686  0.00 0.513  7.083327e-26     6
## DUSP25        3.898464e-04 -0.7650353  0.20 0.506  2.502814e-01     6
## PABPC14       5.745058e-11 -0.7674759  0.04 0.512  3.688327e-08     6
## EZR3          3.878005e-11 -0.7679666  0.04 0.515  2.489680e-08     6
## JUNB5         1.753631e-03 -0.7679670  0.16 0.509  1.000000e+00     6
## TRAT15        8.493584e-61 -0.7701294  0.00 0.518  5.452881e-58     6
## IL2RG4        3.065932e-08 -0.7702873  0.00 0.516  1.968329e-05     6
## VAMP85        8.893476e-09 -0.7742237  0.04 0.510  5.709612e-06     6
## DOK23         1.257208e-15 -0.7882759  0.04 0.510  8.071276e-13     6
## CD65          2.523206e-35 -0.7894006  0.00 0.518  1.619898e-32     6
## TXN6          1.147455e-01 -0.7902001  0.44 0.497  1.000000e+00     6
## PLIN24        7.530824e-15 -0.7955578  0.08 0.513  4.834789e-12     6
## RPS4Y13       2.667144e-08 -0.8070540  0.00 0.518  1.712307e-05     6
## ARPC23        1.603925e-13 -0.8076896  0.00 0.515  1.029720e-10     6
## SH2D1A5       2.071333e-83 -0.8189528  0.00 0.512  1.329796e-80     6
## PLEK5         1.129215e-29 -0.8192987  0.08 0.509  7.249562e-27     6
## SLA5          1.340263e-29 -0.8203127  0.00 0.510  8.604490e-27     6
## CMC16         1.116245e-09 -0.8225596  0.40 0.494  7.166290e-07     6
## CELF21        6.822733e-18 -0.8231366  0.00 0.512  4.380195e-15     6
## NR3C15        2.104167e-21 -0.8256814  0.00 0.513  1.350875e-18     6
## RPL375        5.361029e-07 -0.8272011  0.04 0.512  3.441781e-04     6
## PLAC85        3.297961e-16 -0.8287935  0.16 0.510  2.117291e-13     6
## RPS27A3       4.707681e-06 -0.8314861  0.00 0.516  3.022332e-03     6
## RPLP25        2.679334e-04 -0.8328351  0.00 0.516  1.720132e-01     6
## STK17B3       5.821673e-12 -0.8335580  0.00 0.515  3.737514e-09     6
## RPL304        1.700485e-09 -0.8405044  0.00 0.516  1.091712e-06     6
## RPL344        8.480537e-06 -0.8415243  0.04 0.509  5.444505e-03     6
## GMFG3         6.925447e-12 -0.8422990  0.00 0.510  4.446137e-09     6
## CARD163       2.715656e-15 -0.8478928  0.00 0.513  1.743451e-12     6
## LGALS36       1.525145e-09 -0.8555428  0.20 0.509  9.791430e-07     6
## TAGLN22       3.201532e-09 -0.8627976  0.08 0.509  2.055383e-06     6
## GIMAP44       1.291562e-29 -0.8686836  0.00 0.516  8.291827e-27     6
## RHOG2         1.384864e-24 -0.8715312  0.00 0.515  8.890827e-22     6
## RPS124        3.054346e-05 -0.8740746  0.00 0.513  1.960890e-02     6
## PIK3IP14      8.935202e-27 -0.8899135  0.00 0.518  5.736400e-24     6
## BIRC34        5.457967e-09 -0.8928939  0.04 0.513  3.504015e-06     6
## ITM2A5        5.884994e-08 -0.8976787  0.00 0.515  3.778166e-05     6
## GZMM4         5.369218e-37 -0.9077123  0.00 0.513  3.447038e-34     6
## ARL6IP54      2.931462e-11 -0.9077263  0.04 0.512  1.881999e-08     6
## C12orf754     5.727602e-15 -0.9080070  0.04 0.516  3.677120e-12     6
## MS4A16        1.262521e-07 -0.9111030  0.36 0.475  8.105382e-05     6
## RBPJ4         2.971515e-21 -0.9341364  0.00 0.512  1.907713e-18     6
## UCP23         9.699402e-18 -0.9346417  0.00 0.515  6.227016e-15     6
## ZFP36L25      3.141518e-04 -0.9356649  0.24 0.509  2.016855e-01     6
## TNFRSF1B3     9.030439e-91 -0.9381927  0.00 0.513  5.797542e-88     6
## HMGN22        2.829724e-12 -0.9427418  0.00 0.516  1.816683e-09     6
## ARPC32        4.889568e-11 -0.9454341  0.00 0.516  3.139102e-08     6
## RPL315        1.329928e-05 -0.9505502  0.04 0.515  8.538136e-03     6
## CCR75         1.495623e-47 -0.9514583  0.00 0.510  9.601901e-45     6
## FYN4          3.566711e-31 -0.9517318  0.00 0.515  2.289829e-28     6
## JAML2         1.888873e-49 -0.9657262  0.00 0.513  1.212656e-46     6
## ACP55         3.029231e-19 -0.9666588  0.12 0.507  1.944767e-16     6
## FGFBP26       2.793340e-04 -0.9697351  0.48 0.467  1.793325e-01     6
## GIMAP75       7.191775e-73 -0.9925249  0.00 0.516  4.617120e-70     6
## SELL5         8.091034e-25 -1.0050660  0.08 0.512  5.194444e-22     6
## CD8B6         1.356925e-33 -1.0080619  0.16 0.506  8.711456e-31     6
## TIGIT5        5.950260e-26 -1.0086361  0.04 0.513  3.820067e-23     6
## CALM23        1.066646e-16 -1.0226786  0.00 0.513  6.847866e-14     6
## MSN2          1.076008e-24 -1.0304382  0.00 0.513  6.907970e-22     6
## STMN15        6.989808e-12 -1.0341395  0.08 0.501  4.487456e-09     6
## ICOS3         1.061406e-53 -1.0414461  0.00 0.518  6.814226e-51     6
## CD964         4.586383e-77 -1.0526709  0.00 0.515  2.944458e-74     6
## PTGER44       8.286789e-80 -1.0627930  0.00 0.516  5.320119e-77     6
## SPOCK24       5.774603e-45 -1.0795524  0.00 0.515  3.707295e-42     6
## CKLF3         6.071204e-17 -1.0795632  0.00 0.515  3.897713e-14     6
## ARPC54        4.904500e-25 -1.0905867  0.00 0.516  3.148689e-22     6
## KLRK16        2.023663e-10 -1.0931584  0.00 0.356  1.299192e-07     6
## HOPX6         3.570329e-49 -1.0966535  0.00 0.513  2.292151e-46     6
## DEK1          4.696120e-52 -1.1151489  0.00 0.509  3.014909e-49     6
## RARRES35      1.139490e-16 -1.1228094  0.00 0.515  7.315526e-14     6
## CLEC2B5       2.057437e-17 -1.1347073  0.00 0.516  1.320874e-14     6
## TUBA1B6       4.807777e-15 -1.1561191  0.00 0.513  3.086593e-12     6
## FKBP1A5       2.369565e-18 -1.1598605  0.00 0.516  1.521261e-15     6
## ACTG13        3.993770e-12 -1.1706183  0.00 0.516  2.564000e-09     6
## IFNG6         5.023281e-29 -1.1745354  0.36 0.499  3.224947e-26     6
## CTSW6         6.092432e-08 -1.1867092  0.40 0.500  3.911342e-05     6
## GLUL6         4.478282e-06 -1.1905558  0.12 0.513  2.875057e-03     6
## RPS296        2.213786e-08 -1.2013741  0.04 0.515  1.421250e-05     6
## LIMD24        7.797657e-22 -1.2021907  0.00 0.510  5.006096e-19     6
## C1orf1626    8.674686e-102 -1.2110446  0.00 0.513  5.569149e-99     6
## SAMSN15       9.339964e-16 -1.2268816  0.00 0.518  5.996257e-13     6
## VIM6          1.551495e-07 -1.2336918  0.04 0.513  9.960597e-05     6
## IFITM23       7.716677e-14 -1.2344429  0.00 0.515  4.954106e-11     6
## HMGB24        1.161630e-17 -1.2623084  0.00 0.516  7.457667e-15     6
## FCGR3A5       3.123445e-39 -1.2779834  0.20 0.491  2.005252e-36     6
## KLRD15        3.766648e-33 -1.2921747  0.20 0.485  2.418188e-30     6
## RPS276        5.469406e-08 -1.3012491  0.00 0.516  3.511358e-05     6
## LITAF4        2.110694e-17 -1.3039760  0.04 0.516  1.355065e-14     6
## CAPG6         1.602239e-15 -1.3493052  0.04 0.513  1.028637e-12     6
## CD8A6         2.047033e-34 -1.3932702  0.16 0.506  1.314195e-31     6
## DUSP46        5.657642e-13 -1.3944073  0.12 0.512  3.632206e-10     6
## HLA-DRB56     1.078286e-11 -1.3967064  0.20 0.501  6.922595e-09     6
## NR4A22        2.931229e-18 -1.4050285  0.00 0.512  1.881849e-15     6
## CXCL136       2.320677e-11 -1.4160390  0.52 0.479  1.489875e-08     6
## PRF15         9.141533e-37 -1.4397162  0.04 0.513  5.868864e-34     6
## LCP13         3.678748e-27 -1.4469461  0.00 0.512  2.361756e-24     6
## CTSC5         3.099389e-26 -1.4603950  0.00 0.515  1.989808e-23     6
## ARPC1B4       4.430677e-20 -1.4813097  0.00 0.512  2.844495e-17     6
## MYL12B3       5.072166e-19 -1.4961749  0.00 0.512  3.256330e-16     6
## S100A116      9.456537e-08 -1.5214940  0.24 0.509  6.071097e-05     6
## CRIP13        1.672842e-15 -1.5440199  0.00 0.516  1.073964e-12     6
## TNFAIP34      4.308335e-17 -1.5625897  0.00 0.513  2.765951e-14     6
## CTSD6         3.464528e-07 -1.5901556  0.32 0.503  2.224227e-04     6
## CD746         4.801544e-02 -1.6424463  0.68 0.493  1.000000e+00     6
## ITGB25        3.337078e-37 -1.6597741  0.00 0.515  2.142404e-34     6
## CTSB6         6.612168e-16 -1.6869289  0.48 0.499  4.245012e-13     6
## FYB13         8.078733e-56 -1.7625174  0.00 0.516  5.186546e-53     6
## MYL12A3       3.888990e-23 -1.7695217  0.00 0.515  2.496732e-20     6
## IL7R6         4.387674e-96 -1.7871689  0.00 0.516  2.816887e-93     6
## GPR1834       1.352192e-24 -1.8071553  0.00 0.515  8.681076e-22     6
## MT2A4         6.170958e-12 -1.8264423  0.08 0.513  3.961755e-09     6
## S100A105      2.090137e-14 -1.8348824  0.04 0.516  1.341868e-11     6
## SRGN4         3.612903e-12 -1.8441522  0.00 0.512  2.319483e-09     6
## EVL5          8.195292e-35 -1.8591768  0.00 0.512  5.261377e-32     6
## FTH16         4.732321e-22 -1.8851733  0.00 0.516  3.038150e-19     6
## PFN13         9.163586e-20 -1.9045287  0.00 0.516  5.883022e-17     6
## S100A65       1.119055e-17 -1.9067716  0.00 0.516  7.184334e-15     6
## CCL35         1.365747e-15 -1.9101139  0.32 0.500  8.768096e-13     6
## CD77          5.043268e-22 -1.9229841  0.00 0.513  3.237778e-19     6
## KLRB16        9.469490e-33 -1.9286606  0.04 0.515  6.079412e-30     6
## ANXA14        5.371484e-15 -1.9421527  0.04 0.515  3.448493e-12     6
## ID24          2.778195e-29 -1.9821636  0.00 0.516  1.783601e-26     6
## CST76         9.182405e-31 -2.0094385  0.00 0.515  5.895104e-28     6
## HCST6         3.491690e-32 -2.0241551  0.00 0.513  2.241665e-29     6
## SH3BGRL35     8.750436e-27 -2.0867989  0.00 0.516  5.617780e-24     6
## GZMH6         2.800417e-21 -2.1352991  0.40 0.501  1.797868e-18     6
## LAPTM54       1.591326e-23 -2.1360767  0.00 0.507  1.021631e-20     6
## ALOX5AP6      6.191142e-35 -2.1504060  0.00 0.516  3.974713e-32     6
## CD26          6.051217e-43 -2.2269380  0.00 0.515  3.884882e-40     6
## GZMB6         3.107166e-03 -2.2271816  0.72 0.490  1.000000e+00     6
## ACTB4         2.531622e-21 -2.3035001  0.00 0.516  1.625301e-18     6
## COTL14        2.648829e-43 -2.3752177  0.00 0.516  1.700548e-40     6
## LTB6          1.454379e-57 -2.3918718  0.00 0.518  9.337113e-55     6
## CD3D5         1.631947e-23 -2.4589825  0.00 0.515  1.047710e-20     6
## GZMK6         1.442615e-21 -2.5266194  0.20 0.509  9.261586e-19     6
## S100A44       1.487026e-17 -2.5377583  0.00 0.518  9.546707e-15     6
## CXCR45        7.482497e-17 -2.5388056  0.00 0.518  4.803763e-14     6
## GZMA6         7.796418e-19 -2.5575654  0.32 0.506  5.005301e-16     6
## HLA-DPA16     4.614515e-16 -2.5678377  0.04 0.512  2.962519e-13     6
## CD695         8.719028e-25 -2.7100764  0.00 0.518  5.597616e-22     6
## IL325         1.220594e-16 -2.8888414  0.00 0.518  7.836214e-14     6
## CD525         7.186260e-29 -2.8921922  0.00 0.518  4.613579e-26     6
## CCL46         8.322141e-19 -2.8947683  0.04 0.515  5.342815e-16     6
## GNLY6         5.728150e-07 -2.9309349  0.60 0.490  3.677472e-04     6
## TYROBP6       4.798813e-30 -3.1030173  0.48 0.481  3.080838e-27     6
## HLA-DRB15     2.089168e-16 -3.2137052  0.04 0.516  1.341246e-13     6
## NKG76         1.359669e-22 -3.2654340  0.36 0.503  8.729077e-20     6
## CCL56         2.226293e-26 -3.7555082  0.12 0.510  1.429280e-23     6
## HLA-DRA6      1.072618e-31 -4.2191545  0.00 0.518  6.886209e-29     6
##                     gene
## HERPUD15         HERPUD1
## HSPB15             HSPB1
## ITM2C6             ITM2C
## PIM24               PIM2
## PDIA63             PDIA6
## RABAC15           RABAC1
## JUN3                 JUN
## GSTP15             GSTP1
## IFI64               IFI6
## CD381               CD38
## COPE2               COPE
## CCNL1              CCNL1
## ISG204             ISG20
## SLAMF73           SLAMF7
## SLC3A21           SLC3A2
## CD276               CD27
## DAD11               DAD1
## PRDX52             PRDX5
## BST24               BST2
## MALAT13           MALAT1
## NME11               NME1
## GLRX6               GLRX
## TPST23             TPST2
## CD79B6             CD79B
## OST41               OST4
## VSIR4               VSIR
## SUB14               SUB1
## COX5A3             COX5A
## CYTOR6             CYTOR
## FDPS                FDPS
## COX7A21           COX7A2
## TRMT1122         TRMT112
## RPLP04             RPLP0
## CALR3               CALR
## RPL36AL1         RPL36AL
## UBB5                 UBB
## CD636               CD63
## MRPL511           MRPL51
## CCND24             CCND2
## CDK2AP21         CDK2AP2
## DSTN4               DSTN
## PDIA32             PDIA3
## MIF                  MIF
## PSMD82             PSMD8
## IFI351             IFI35
## TRADD              TRADD
## NCF42               NCF4
## RPL85               RPL8
## DDIT44             DDIT4
## PRDX15             PRDX1
## RPL7A3             RPL7A
## PSMB62             PSMB6
## NDUFA11           NDUFA1
## NDUFA41           NDUFA4
## ISG153             ISG15
## CENPX1             CENPX
## PSME23             PSME2
## VCP2                 VCP
## PLP22               PLP2
## RPLP13             RPLP1
## SURF44             SURF4
## KIF22              KIF22
## UQCRC13           UQCRC1
## COX5B3             COX5B
## KIAA15512       KIAA1551
## RPS144             RPS14
## ATP1B35           ATP1B3
## P2RY102           P2RY10
## IFI441             IFI44
## LEF13               LEF1
## ANXA62             ANXA6
## NABP12             NABP1
## ZFP364             ZFP36
## TMEM1233         TMEM123
## A2M4                 A2M
## LINC008923     LINC00892
## EIF4A34           EIF4A3
## PSMA44             PSMA4
## SRP141             SRP14
## SASH3              SASH3
## PNISR2             PNISR
## RPS184             RPS18
## NOP533             NOP53
## LINC008613     LINC00861
## PTGER21           PTGER2
## EIF3L2             EIF3L
## GBP53               GBP5
## ANP32B            ANP32B
## ITGA15             ITGA1
## RPS104             RPS10
## RAN2                 RAN
## NFKBIA6           NFKBIA
## EIF3E4             EIF3E
## TSEN542           TSEN54
## APMAP5             APMAP
## PA2G42             PA2G4
## RGS15               RGS1
## OASL4               OASL
## STAT12             STAT1
## TCF72               TCF7
## LPXN1               LPXN
## COX6C1             COX6C
## TPI15               TPI1
## CXCR34             CXCR3
## KPNA22             KPNA2
## RPL134             RPL13
## SLBP2               SLBP
## RPS65               RPS6
## CRYBG1            CRYBG1
## PDCD14             PDCD1
## RPS163             RPS16
## KLRF15             KLRF1
## TMC81               TMC8
## PRKCQ-AS13     PRKCQ-AS1
## RHBDD22           RHBDD2
## PPP1CA5           PPP1CA
## PCNA3               PCNA
## CLDND13           CLDND1
## FCMR4               FCMR
## ANP32E3           ANP32E
## SIT13               SIT1
## LAIR25             LAIR2
## PCLAF4             PCLAF
## MYL64               MYL6
## RPL194             RPL19
## ARHGAP152       ARHGAP15
## RPS26               RPS2
## TBC1D43           TBC1D4
## CRTAM6             CRTAM
## KLRC25             KLRC2
## DENND2D3         DENND2D
## GSTK13             GSTK1
## OCIAD23           OCIAD2
## AOAH5               AOAH
## SERINC52         SERINC5
## CNN22               CNN2
## XIST1               XIST
## GALM3               GALM
## CLIC34             CLIC3
## TNFRSF95         TNFRSF9
## FLNA3               FLNA
## SIRPG4             SIRPG
## DUT1                 DUT
## FGR5                 FGR
## SERPINB93       SERPINB9
## CD40LG4           CD40LG
## RPL23A4           RPL23A
## ZNF6835           ZNF683
## CD582               CD58
## PTTG15             PTTG1
## CD441               CD44
## UGP22               UGP2
## HPRT11             HPRT1
## TANK3               TANK
## CD56                 CD5
## APOBEC3C4       APOBEC3C
## RPL143             RPL14
## SOD13               SOD1
## CBLB3               CBLB
## IFITM15           IFITM1
## CTSA3               CTSA
## SOCS14             SOCS1
## NPM13               NPM1
## SLC25A34         SLC25A3
## HNRNPM            HNRNPM
## SAMD35             SAMD3
## PBXIP14           PBXIP1
## TPP14               TPP1
## STAT43             STAT4
## RPL274             RPL27
## RPL13A3           RPL13A
## CASP13             CASP1
## IFNGR13           IFNGR1
## IL2RA6             IL2RA
## YWHAQ3             YWHAQ
## OXNAD14           OXNAD1
## CTLA43             CTLA4
## ITK4                 ITK
## PSMA73             PSMA7
## TMEM50A3         TMEM50A
## RPL10A4           RPL10A
## GSTO16             GSTO1
## NINJ15             NINJ1
## TNFAIP81         TNFAIP8
## H2AFY5             H2AFY
## TNFRSF255       TNFRSF25
## JPT14               JPT1
## TUBB5               TUBB
## FKBP51             FKBP5
## RPS235             RPS23
## TRIM22            TRIM22
## UBE2N4             UBE2N
## RPL115             RPL11
## GABARAPL13     GABARAPL1
## LDHB3               LDHB
## BIN23               BIN2
## RPS135             RPS13
## RPL364             RPL36
## APOBEC3G6       APOBEC3G
## ARGLU12           ARGLU1
## PRNP1               PRNP
## RPL233             RPL23
## HNRNPH12         HNRNPH1
## PTPN224           PTPN22
## RIPOR23           RIPOR2
## LYAR5               LYAR
## RPL264             RPL26
## KLRG15             KLRG1
## RNF19A4           RNF19A
## TNFRSF185       TNFRSF18
## SNRPB2             SNRPB
## ANXA55             ANXA5
## HNRNPF1           HNRNPF
## STAT34             STAT3
## ABI32               ABI3
## RPS204             RPS20
## RHOA5               RHOA
## PRR13              PRR13
## SYTL34             SYTL3
## IVNS1ABP2       IVNS1ABP
## AP2S16             AP2S1
## TSC22D33         TSC22D3
## PAXX3               PAXX
## MCUB1               MCUB
## OSTF12             OSTF1
## ENO15               ENO1
## XCL25               XCL2
## PDCD43             PDCD4
## RHOC4               RHOC
## SH2D2A4           SH2D2A
## KLRC15             KLRC1
## ZYX2                 ZYX
## EFHD25             EFHD2
## TRAF3IP34       TRAF3IP3
## UBA522             UBA52
## RPS215             RPS21
## CD824               CD82
## TMEM1735         TMEM173
## FTL6                 FTL
## TOB13               TOB1
## TXNIP4             TXNIP
## PGK13               PGK1
## CD46                 CD4
## PYHIN14           PYHIN1
## ADGRE55           ADGRE5
## HAVCR24           HAVCR2
## RPS254             RPS25
## RPS15A3           RPS15A
## RPL124             RPL12
## GAPDH5             GAPDH
## RAP1B2             RAP1B
## ABRACL3           ABRACL
## SARAF3             SARAF
## RPL324             RPL32
## LCP22               LCP2
## CXCR65             CXCR6
## LDHA3               LDHA
## LAG36               LAG3
## HLA-DRB65       HLA-DRB6
## MYO1F3             MYO1F
## IL2RB4             IL2RB
## LRRC75A-AS14 LRRC75A-AS1
## HNRNPA2B11     HNRNPA2B1
## AES4                 AES
## ARL6IP12         ARL6IP1
## ITGB13             ITGB1
## GPR1716           GPR171
## CDC42SE1        CDC42SE1
## PMAIP14           PMAIP1
## PIP4K2A4         PIP4K2A
## HMGN12             HMGN1
## TNFRSF46         TNFRSF4
## CCND32             CCND3
## BATF5               BATF
## GBP23               GBP2
## RPS33               RPS3
## GAS53               GAS5
## SAMHD15           SAMHD1
## DUSP25             DUSP2
## PABPC14           PABPC1
## EZR3                 EZR
## JUNB5               JUNB
## TRAT15             TRAT1
## IL2RG4             IL2RG
## VAMP85             VAMP8
## DOK23               DOK2
## CD65                 CD6
## TXN6                 TXN
## PLIN24             PLIN2
## RPS4Y13           RPS4Y1
## ARPC23             ARPC2
## SH2D1A5           SH2D1A
## PLEK5               PLEK
## SLA5                 SLA
## CMC16               CMC1
## CELF21             CELF2
## NR3C15             NR3C1
## RPL375             RPL37
## PLAC85             PLAC8
## RPS27A3           RPS27A
## RPLP25             RPLP2
## STK17B3           STK17B
## RPL304             RPL30
## RPL344             RPL34
## GMFG3               GMFG
## CARD163           CARD16
## LGALS36           LGALS3
## TAGLN22           TAGLN2
## GIMAP44           GIMAP4
## RHOG2               RHOG
## RPS124             RPS12
## PIK3IP14         PIK3IP1
## BIRC34             BIRC3
## ITM2A5             ITM2A
## GZMM4               GZMM
## ARL6IP54         ARL6IP5
## C12orf754       C12orf75
## MS4A16             MS4A1
## RBPJ4               RBPJ
## UCP23               UCP2
## ZFP36L25         ZFP36L2
## TNFRSF1B3       TNFRSF1B
## HMGN22             HMGN2
## ARPC32             ARPC3
## RPL315             RPL31
## CCR75               CCR7
## FYN4                 FYN
## JAML2               JAML
## ACP55               ACP5
## FGFBP26           FGFBP2
## GIMAP75           GIMAP7
## SELL5               SELL
## CD8B6               CD8B
## TIGIT5             TIGIT
## CALM23             CALM2
## MSN2                 MSN
## STMN15             STMN1
## ICOS3               ICOS
## CD964               CD96
## PTGER44           PTGER4
## SPOCK24           SPOCK2
## CKLF3               CKLF
## ARPC54             ARPC5
## KLRK16             KLRK1
## HOPX6               HOPX
## DEK1                 DEK
## RARRES35         RARRES3
## CLEC2B5           CLEC2B
## TUBA1B6           TUBA1B
## FKBP1A5           FKBP1A
## ACTG13             ACTG1
## IFNG6               IFNG
## CTSW6               CTSW
## GLUL6               GLUL
## RPS296             RPS29
## LIMD24             LIMD2
## C1orf1626       C1orf162
## SAMSN15           SAMSN1
## VIM6                 VIM
## IFITM23           IFITM2
## HMGB24             HMGB2
## FCGR3A5           FCGR3A
## KLRD15             KLRD1
## RPS276             RPS27
## LITAF4             LITAF
## CAPG6               CAPG
## CD8A6               CD8A
## DUSP46             DUSP4
## HLA-DRB56       HLA-DRB5
## NR4A22             NR4A2
## CXCL136           CXCL13
## PRF15               PRF1
## LCP13               LCP1
## CTSC5               CTSC
## ARPC1B4           ARPC1B
## MYL12B3           MYL12B
## S100A116         S100A11
## CRIP13             CRIP1
## TNFAIP34         TNFAIP3
## CTSD6               CTSD
## CD746               CD74
## ITGB25             ITGB2
## CTSB6               CTSB
## FYB13               FYB1
## MYL12A3           MYL12A
## IL7R6               IL7R
## GPR1834           GPR183
## MT2A4               MT2A
## S100A105         S100A10
## SRGN4               SRGN
## EVL5                 EVL
## FTH16               FTH1
## PFN13               PFN1
## S100A65           S100A6
## CCL35               CCL3
## CD77                 CD7
## KLRB16             KLRB1
## ANXA14             ANXA1
## ID24                 ID2
## CST76               CST7
## HCST6               HCST
## SH3BGRL35       SH3BGRL3
## GZMH6               GZMH
## LAPTM54           LAPTM5
## ALOX5AP6         ALOX5AP
## CD26                 CD2
## GZMB6               GZMB
## ACTB4               ACTB
## COTL14             COTL1
## LTB6                 LTB
## CD3D5               CD3D
## GZMK6               GZMK
## S100A44           S100A4
## CXCR45             CXCR4
## GZMA6               GZMA
## HLA-DPA16       HLA-DPA1
## CD695               CD69
## IL325               IL32
## CD525               CD52
## CCL46               CCL4
## GNLY6               GNLY
## TYROBP6           TYROBP
## HLA-DRB15       HLA-DRB1
## NKG76               NKG7
## CCL56               CCL5
## HLA-DRA6         HLA-DRA

using the scatter plot to check CD4 or CD8

make_dotplot <- function(object, genes, cells.use = NULL) {
    if (is.null(cells.use)) {
        cells.use = colnames(object@scale.data)
    }
    
    data = object@scale.data[genes, cells.use]
    data = as.data.frame(t(data))
    # data = melt(as.matrix(data))
    
    p <- eval(
        parse(text = paste0(
            "ggplot(data = data, aes(x=", genes[1], ", y=", genes[2], "))"
        ))
    )
    
    p = p + geom_point() + geom_abline(color="red", linetype = "dashed")
    
    p
}


for (i in 1:4) {
    p1 <- make_dotplot(obj, genes=c("CD8A", "CD4"), cells.use = rownames(obj@meta.data)[obj@meta.data$res.0.6 == i])
    p2 <- make_dotplot(obj, genes=c("CD8B", "CD4"), cells.use = rownames(obj@meta.data)[obj@meta.data$res.0.6 == i])
    
    p <- plot_grid(p1, p2, nrow = 1)
    
    title = ggdraw() + draw_label(paste0("cluster ", i))
    
    p <- plot_grid(title, p, ncol = 1, rel_heights = c(0.1, 1))
    
    print(p)
}

通过基因染色可认为 - 1为CD4 - 2为CD8 - 3为CD8 - 4为CD4

而上方散点图可见 - 3基本为CD8 - 4基本为CD4 - 而1和2为混合细胞 - 其中2偏向CD8

Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot.